--- license: cc-by-4.0 task_categories: - translation - text-generation language: - en - it tags: - gender - inclusivity - ethics - fairness - mt - neomorphemes multilinguality: - multilingual - translation pretty_name: Neo-GATE size_categories: - n<1K --- # Dataset card for Neo-GATE **Homepage:** [https://mt.fbk.eu/neo-gate/](https://mt.fbk.eu/neo-gate/) ## Dataset summary Neo-GATE is a bilingual corpus designed to benchmark the ability of machine translation (MT) systems to translate from English into Italian using gender-inclusive neomorphemes. It is built upon GATE [(Rarrick et al., 2023)](https://dl.acm.org/doi/10.1145/3600211.3604675), a benchmark for the evaluation of gender rewriters and gender bias in MT. Neo-GATE includes 841 `test` entries and 100 `dev` entries. Each entry is composed of an English source sentence, three Italian references which only differ in the gendered terms, and an annotation that identifies the words of interest for gender-inclusive MT evaluation. The source sentences are gender-ambiguous, i.e. they provide no information about the gender of human referents. In our gender-inclusive MT task, words referring to human entities in the target language should express gender with neomorphemes, special characters or symbols that replace masculine and feminine inflectional morphemes. Neo-GATE allows for the evaluation of any neomorpheme paradigm in Italian. For more details see the [Adaptation](#adaptation) section below. ## Data Fields `Neo-GATE.tsv` includes the following columns: - **#:** The number of the entry within Neo-GATE. - **GATE-ID:** The ID of the original entry in GATE, composed of a prefix indicating the subset of origin within GATE (e.g., `IT_2_variants`) followed by a serial number indicating the position of the entry within that subset (i.e., `001`, `002`, etc.). - **SPLIT:** Either `dev` or `test`, indicating whether the entry belongs to the dev set or the test set. - **SOURCE:** The English source sentence. - **REF-M:** The Italian reference where all gender-marked terms are masculine. - **REF-F:** The Italian reference where all gender-marked terms are feminine. - **REF-TAGGED:** The Italian reference where all gender-marked terms are tagged with Neo-GATE's annotation. - **ANNOTATION:** The annotation for that entry. ## Dataset creation Please refer to [the original paper](https://arxiv.org/search/?searchtype=author&query=Piergentili%2C+A) for full details on dataset creation. ## Adaptation To adapt Neo-GATE to the desired neomorpheme paradigm, a `.json` file mapping Neo-GATE's tagset to the desired forms is required. See `schwa.json` for an example. For more information on the tagset, see Table 8 in [the original paper](https://arxiv.org/search/?searchtype=author&query=Piergentili%2C+A). To create the adapted references and annotations, use the `neo-gate_format.py` script with the following syntax: python neo-gate_adapt.py --tagset JSON_FILE_PATH --out OUTPUT_FILE_NAME This command will create two files: `OUTPUT_FILE_NAME.ref`, containing the adapted references, and `OUTPUT_FILE_NAME.ann`, containing the adapted annotations. For instance, to generate the references and the annotations adapted to the schwa paradigm provided in the example file `schwa.json`, the following command can be used: python neo-gate_adapt.py --tagset schwa.json --out neogate_schwa This will create the two files `neogate_schwa.ref` and `neogate_schwa.ann`. If the `Neo-GATE.tsv` file is located in a different directory, the path to it can be passed to the script with the optional argument `--neogate`. ## Evaluation The evaluation code is available at [fbk-NEUTR-evAL](https://github.com/hlt-mt/fbk-NEUTR-evAL/tree/main). ## Licensing Information The Neo-GATE corpus is released under a Creative Commons Attribution 4.0 International license (CC BY 4.0). ## Citation If you use Neo-GATE in your work, please consider citing the following paper: